The main idea behind any image classification process is to obtain highest accuracy possible. Minimum distance and parallelepiped method yielded acceptable results for image classification but they are bounded by their inherent limitations. On the other hand, fuzzy based systems are fast and provide good accuracy. In fuzzy, accuracy depends upon the type of membership function used, and how the membership functions in the output of FIS are arranged. In this paper Mamdani fuzzy inference system is used to classify image and how the arrangements and the type of fuzzy membership functions employed in the classification, affected the results obtained, are shown.
Key words: Image classification, fuzzy logic, type of membership function, positioning of membership functions
Copyright © 2021 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0